Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data

Frontiers in Medicine

article

Huiqing Ge¹  Limin Liu²  Zhongheng Zhang³  Qing Pan⁴  Yong Zhou⁵  Peifeng Xu¹  Lingwei Zhang⁴  Junli Zhang⁶  Jun Yi⁷  Changming Yang⁸  Yuhan Zhou⁴ 

[1] Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine;Department of Administration, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine;Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine;College of Information Engineering, Zhejiang University of Technology;Department of Pulmonary Disease, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine;Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine;Jingmen First People's Hospital;Department of Anesthesiology, The First People's of Hospital of Jingmen City

关键词: COVID-19;    lung mechanics;    mechanical ventilation;    asynchrony;    asynchonized;    prone positioning;   

DOI  :  10.3389/fmed.2020.00541

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: −0.90; 95% CI: −1.02 to −0.78) and neuromuscular blockades (Coefficient: −6.54; 95% CI: −9.92 to −3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH 2 O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

【 授权许可】

CC BY